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Overview and advancement of power system topology addressing pre- and post-event strategies under abnormal operating conditions

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2024年 40卷

作者： Yaldiz, Abdurahman Gokcek, Tayfur Ates, Yavuz Erdinc, Ozan Yildiz Tech Univ Dept Elect Engn Istanbul Turkiye

The transition towards increased utilization of renewable energy and electric vehicles (EVs), along with the growing use of various other electrical devices, poses challenges to the stable and resilient operation of electric power systems (EPS), especially in the face of natural phenomena associated with climate change. This means that accurate topology and balanced EPS plays a key role to increase the capacity to respond quickly and in a coordinated manner to disaster situations such as cyber-attacks, earthquakes and floods. In this study, a new approach is presented to quickly and accurately detect topology attacks in EPS, thus contributing to making safer and more resilient. The proposed methods provide insights into maintaining uninterrupted electricity service by enabling EPS management through both post- and pre-event operational strategies. This approach is created by identifying faulty points with the obtained topology information and creating microgrid (MG) groups. Machine learning techniques have been integrated into the data intrusion attack detection (DIAD) system, enabling the detection of manipulated or faulty smart meters (SM). Concurrently, a topology identification (TI)-based graph learning algorithm is propounded to determine the exact fault locations before and after the event. For MV region restoration after determining the TI region, a mixed-integer linear programming (MILP) approach is employed to optimize the load restoration process in the MG regions. This approach aims to minimize losses and restore critical loads to their previous state as quickly as possible using flexible and emergency power balancing systems, including grid-support storage systems (GSSs), photovoltaic systems (PVs), electric vehicle charging stations (EVCS), and mobile generators. Moreover, a detailed compilation is presented under the topics of EPS topology, phase identification (PI) and its effect on power system resiliency (PSR), shedding light on the future developme

关键词： Smart grid Phase identification Power system resiliency Grid support storage system Topology identification Optimization mixed-integer linear programming Machine learning

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Function-dependent neural-network-driven state feedback control and self-verification stability for discrete-time nonlinear system

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NEUROCOMPUTING 2024年 609卷

作者： Wang, Jingya Feng, Xiao Yu, Yongbin Wang, Xiangxiang Han, Xinyi Shi, Kaibo Zhong, Shouming Shen, Jiarun Cai, Jingye Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu 610054 Sichuan Peoples R China Chengdu Univ Sch Elect Informat & Elect Engn Chengdu 610106 Sichuan Peoples R China Univ Elect Sci & Technol China Sch Math Sci Chengdu 611731 Sichuan Peoples R China

Deep learning significantly impacts neural network controller synthesis. Despite the higher efficiency of deep learning algorithms compared to traditional model-based controller design methods, the performance of these neural network controllers lacks theoretical guarantees. Initially, this paper proposes an architecture to synthesize the state feedback controller and Lyapunov function, called function-dependent neural-network- driven (NN-driven) architecture. The discrete-time nonlinear system dynamics, state feedback controller and Lyapunov function in this architecture are all designed using neural networks, which can improve the efficiency of stability verification and control performance. To realize self-verification of system stability by neural networks, three optimization problems are designed based on Lyapunov stability conditions and training speed constraints, solved using mixed-integer linear programming (MILP) solvers. Additionally, a MILP-based algorithm called Stability Counter-examples Updating Training Set (SCUTS) is proposed to simultaneously train the state feedback controller and Lyapunov function neural networks. Finally, experiments conducted on a second-order discrete-time nonlinear system, an inverted pendulum with NN-driven dynamics and an inverted pendulum with Lagrangian dynamics demonstrate the effectiveness of this work. Experimental results indicate that this work outperforms previous research on synthesizing neural network controllers in both the neural network training speed and the control system stabilization speed.

关键词： Function-dependent Neural-network-driven Self-verification Lyapunov stability mixed-integer linear programming Nonlinear system

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A distributionally robust approach for the parallel machine scheduling problem with optional machines and job tardiness

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COMPUTERS & OPERATIONS RESEARCH 2024年 170卷

作者： Lu, Haimin Shi, Ye Pei, Zhi Zhejiang Univ Technol Coll Mech Engn Hangzhou 310023 Peoples R China

This paper investigates a parallel machine scheduling problem with uncertain job processing time, where the job tardiness and optional machines are considered. To address the factor of energy saving, only a subset of all available machines are turned on, which is referred to as not-all-machine (NAM). To depict the uncertain processing time, a mean-mean absolute deviation (MAD) ambiguity set is utilized, and the cost of job tardiness is minimized under the worst-case distribution scenario over the ambiguity set. After building a distributionally robust optimization (DRO) model, theoretical bounds of the optimal number of machines are obtained. Since the model is not computationally scalable, an upper bound on its inner minimization problem is employed, and a mixed integer linear programming (MILP) approximation is obtained based on McCormick inequalities. For the DRO model, tailored speedup techniques are employed, significantly enhancing the computational performance. To evaluate the validity of the proposed DRO model, we compare it with its stochastic programming (SP) counterpart under various parameter settings. Numerical experiments demonstrate that the DRO model exhibits strong performance in the worst-case scenarios. As the problem size increases, the DRO model casts clear advantages over the SP model in terms of computational efficiency and reliability. It is observed that the performance of the DRO model is more stable than that of the nominal sequence, especially with loose due dates. Furthermore, the out-of-sample performance under various decision making preferences shed new lights into the trade-off between energy saving and production efficiency.

关键词： Parallel machine scheduling Optional machines Tardiness Distributionally robust optimization mixed-integer linear programming

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Energy-efficient train control incorporating inherent reduced-power and hybrid braking characteristics of railway vehicles

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 163卷

作者： Peng, Yang Lu, Shaofeng Chen, Feng Liu, Xiao Tian, Zhongbei South China Univ Technol Shien Ming Wu Sch Intelligent Engn Guangzhou Peoples R China Univ Liverpool Sch Elect Engn & Elect Liverpool England Univ Birmingham Sch Elect Elect & Syst Engn Birmingham England

With the increasing awareness of carbon neutrality, the application of energy -efficient train control (EETC) to rail transportation systems continues to attract attention from industry and academia. In many classic EETC studies, train models are commonly simplified with pure regenerative braking and constant power characteristics during high speeds, to simplify the complexity of the model. In this paper, a realistic model incorporating hybrid braking characteristics combining regenerative and mechanical braking, and reduced -power characteristics at high speeds into the EETC problem is proposed to improve control precision of the train and the modeling precision of the energy consumption and time. This study addresses the minimum -time train control (MTTC) problem and EETC problem considering nonlinear traction characteristics based on the mixed -integer linear programming (MILP) method, and nonlinear traction and braking characteristics are approximated via a piecewise linear (PWL) modeling technique. Results indicate that the proposed alternative models achieves some deviations from the realistic model in terms of time, energy consumption, and control strategies. The deviations between models in energy consumption and time accumulate as the number of operating stations increases. Therefore, the choice of an appropriate model depends on the precision requirements of various scenarios. In scenarios demanding higher precision, selecting the proposed realistic model is crucial for more accurate computation of energy consumption and time and for obtaining more precise control strategies.

关键词： Energy-efficient train control Minimum-time train control Reduced-power characteristics Hybrid braking characteristics mixed-integer linear programming Railway vehicles

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Optimization of battery energy storage system (BESS) sizing in different electricity market types considering BESS utilization mechanisms and ownerships

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JOURNAL OF CLEANER PRODUCTION 2024年 470卷

作者： Zhang, Ruixiaoxiao Lee, Minhyun Huang, Lijie Ni, Meng Hong Kong Polytech Univ Dept Bldg & Real Estate Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Res Inst Smart Energy RISE Dept Bldg & Real Estate Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Res Inst Sustainable Urban Dev RISUD Dept Bldg & Real Estate Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Res Inst Smart Energy RISE Kowloon Hong Kong Peoples R China

To elucidate the optimal techno-economic role of battery energy storage system (BESS), this study proposes optimal sizing of BESS in various scenarios based on BESS installation in existing photovoltaic systems. The proposed scenarios include different electricity market types (i.e., peer-to-grid, peer-to-peer, and energy storage sharing) considering utilization mechanism (i.e., individual-design or shared-design) and ownership (i.e., user- owned or developer-owned) of the BESS. Towards this end, this study develops comprehensive and systematic mathematical models for BESS sizing using mixed-integer linear programming algorithm, and the BESS sizing is optimized to maximize the total net present value. The developed models are validated in the Hong Kong context considering four peers, and the empirical data of electric load profile as well as the photovoltaic installed capacity are collected. As a result, it is indicated that the optimal BESS capacity in energy storage sharing scenario is the least. In terms of electricity bill saving, user-owned BESS is regarded as the model yielding the highest electricity bill savings. The breakdown of net present value exposes that under the same ownership, the optimal BESS capacity is determined by different utilization mechanisms. Regarding the total net present value, it is found that developer-owned BESS could achieve the maximum economic merits. Nonetheless, the distribution loss through energy storage sharing is also the highest. The policy implications of this study primarily emphasize incentivizing user-owned BESS, promoting energy storage sharing, supporting shared BESS infrastructure, and encouraging a diversity of electricity trading mechanisms to enhance the integration and efficiency of renewable energy systems.

关键词： Battery energy storage system Sizing optimization mixed-integer linear programming Utilization mechanism and ownership Peer trading Energy storage sharing

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Energy management of space-heating systems and grid-connected batteries in smart homes

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ENERGY ECOLOGY AND ENVIRONMENT 2022年第1期7卷 37-50页

作者： Al Essa, Mohammed Jasim M. Univ Kufa Fac Engn POB 21 Najaf Governorate Iraq

Several approaches of energy management systems reduce power consumption of heating demand and electricity storage based on static or dynamic tariffs. However, such methodologies impose uncertainties due to forecasting errors of energy consumption and generation, while evaluating electricity prices. Alternatively, this paper proposes a novel methodology of residential energy management to decrease electricity consumption of space-heating units and grid-connected batteries without incorporating price signals, while maintaining their characteristic operation. The proposed algorithm of energy management develops seasonal calculations of heating load and storage power to achieve energy savings in smart homes based on mixed-integer linear programming, considering photovoltaic electric generation. Power consumption of heating systems is estimated considering heat losses of conduction and ventilation through buildings in addition to other important parameters such as outdoor and indoor temperatures. Charging and discharging patterns of grid-connected batteries are modelled consistent with residential loads. Simulation results show that the proposed algorithm of energy management is able to reduce energy consumption of space-heating loads by 15%, mitigating their environmental impact while keeping their functioning usage. Moreover, the algorithm decreases charging demand of grid-connected batteries by 13%, maintaining their state-of-charge levels between 10 and 90%.

关键词： Energy storage systems Grid-level batteries mixed-integer linear programming Photovoltaics Smart energy management Space heating Thermostatically controlled loads

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A new mixed-integer linear programming model for rescue path planning in uncertain adversarial environment

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COMPUTERS & OPERATIONS RESEARCH 2012年第12期39卷 3420-3430页

作者： Berger, Jean Boukhtouta, Abdeslem Benmoussa, Abdelhamid Kettani, Ossama Def R&D Canada Valcartier Quebec City PQ Canada Def R&D Canada CORA Ottawa ON Canada Modellium Inc Quebec City PQ Canada

Efficient vehicle path planning in hostile environment to carry out rescue or tactical logistic missions remains very challenging. Most approaches reported so far rely on key assumptions and heuristic procedures to reduce problem complexity. In this paper, a new model is proposed to solve the discrete rescue path planning problem for a single agent navigating in uncertain adversarial environment. It relies on a novel and simplified mathematical mixed-integer linear programming formulation aimed at minimizing traveled distance and threat exposure. Exploiting a user-defined survivability function approximation and survivability threshold, the approximate model allows constructing a solution providing an adjustable optimality gap interval on the optimal solution. Experimental results show the value of the proposed approach in computing near optimal solutions reasonably fast for various problem instances. Crown Copyright (c) 2012 Published by Elsevier Ltd. All rights reserved.

关键词： Path planning Constrained shortest path mixed-integer linear programming

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Strategic optimization and design of cost-effective and sustainable hydrogen supply chain networks - Qatar case study

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ENERGY CONVERSION AND MANAGEMENT 2025年 327卷

作者： Amjath, Mohamed Eljack, Fadwa Haouari, Mohamed Qatar Univ Chem Engn Dept Coll Engn POB 2713 Doha Qatar Qatar Univ Coll Engn Dept Mech & Ind Engn POB 2713 Doha Qatar

This study introduces a multi-period integrated optimization model for designing a strategic hydrogen supply chain (HSC) network, concentrating on the post-production stages of conditioning, storage, transportation, and post-conditioning. Qatar serves as the case study for evaluating three HSC pathways-ammonia (as a hydrogen carrier), liquefied hydrogen, and compressed hydrogen-across pre-conditioning, storage, shipping, and post- conditioning stages. The optimization framework spans a 20-year plan, supporting strategic, long-term hydrogen export infrastructure planning. Economic and environmental factors are incorporated to analyze HSC performance under various scenarios, accounting for realistic constraints, such as investment limits and emission caps. Key findings reveal trade-offs between pathways and design strategies that must account for balancing costs with environmental impacts. Results indicate that the ammonia pathway is preferred in scenarios without emission penalties but becomes less favorable with increased penalties, shifting preference toward the liquified hydrogen pathway. With stringent emission limits, short- and mid-range markets are prioritized, underscoring the importance of emissions-conscious strategies. This study demonstrates the utility of optimization tools in balancing economic and environmental objectives, offering policymakers and industry stakeholders a robust framework for developing sustainable and efficient HSC networks.

关键词： Hydrogen Supply chain management Optimization mixed-integer linear programming Hydrogen economy

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An MILP Model for Optimal Placement of Sectionalizing Switches and Tie Lines in Distribution Networks With Complex Topologies

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IEEE TRANSACTIONS ON SMART GRID 2021年第6期12卷 4740-4751页

作者： Jooshaki, Mohammad Karimi-Arpanahi, Sahand Lehtonen, Matti Millar, R. John Fotuhi-Firuzabad, Mahmud Geologian Tutkimuskeskus Circular Econ Solut Unit Espoo 02151 Finland Aalto Univ Dept Elect Engn & Automat Espoo 02150 Finland Univ Adelaide Sch Elect & Elect Engn Adelaide SA 5005 Australia Sharif Univ Technol Elect Engn Dept Tehran Iran

Sectionalizing switches (SSs) and tie lines play essential roles in reducing the duration of customer interruptions in electricity distribution networks. The effectiveness of such assets is strongly influenced by their placement in the grid. Operation of SSs and tie lines is also inherently interdependent. Due to the structural complexities regarding the mathematical modeling of such dependencies, optimization of the planning and operation of switches and tie lines has typically required either leveraging heuristic and metaheuristic approaches or oversimplifying the network topology. To tackle such issues, this paper presents a computationally-efficient model for reliability-oriented concurrent switch and tie line placement in distribution networks with complex topologies. The proposed model can be applied to grids with several tie lines and laterals per feeder, and yields the optimal location of tie lines, type of tie switches, namely manual or remote-controlled, and the location and type of SSs. Being cast as a mixed integer linear programming (MILP) problem, the model can be efficiently solved with guaranteed convergence to global optimality using off-the-shelf optimization software. The efficiency and scalability of the proposed model are demonstrated through implementation on five networks and the outcomes are thoroughly discussed.

关键词： Optimization Reliability Indexes Topology Resource management Network topology Manuals Electricity distribution mixed-integer linear programming optimization reliability switch tie line

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A multi-objective model for publicly funded festival planning

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2024年第0期

作者： Alcada-Almeida, L. Sousa, N. Coutinho-Rodrigues, J. INESC Coimbra P-3030790 Coimbra Portugal Univ Coimbra Fac Econ P-3004512 Coimbra Portugal Univ Aberta Dept Sci & Technol P-1269001 Lisbon Portugal Univ Coimbra Fac Sci & Technol Dept Civil Engn P-3004512 Coimbra Portugal

This article proposes a multi-objective mixed-integer linear programming model to assist event managers in obtaining and evaluating non-dominated solutions to the problem of selecting a daily lineup of shows and activities for a festival - be it cultural, sports, ceremonial or any other kind. The model, which is especially adequate for designing festivals with public funding, has five objectives, relating to financial, logistical and how renowned the festival artists or acts are. It includes support for multiple days, multiple stages and different types of shows, all subject to constraints imposed by the intrinsic nature of the festival itself. The output of the model is a set of optimized daily lineups for the activities that constitute the festival, each corresponding to a particular compromise between the five objectives. The approach is demonstrated with a case study for a 5-day festival, for which non-dominated solutions are derived, presented and discussed. Results show that the model can provide a good variety of solutions while ensuring the persistence of the more desirable shows. The model is a novel decision support tool to assist in designing festival lineups that provide optimal audience experience, a key factor in attracting spectators, tourists and increasing comeback value.

关键词： event and festival planning multi-objective optimization mixed-integer linear programming cultural event management

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